Utilising near-infrared hyperspectral imaging to detect low-level peanut powder contamination of whole wheat flour

Abstract

Near-infrared hyperspectral imaging (HSI) was used for detecting low levels of peanut powder contamination in whole wheat flour, with concentrations of 0.01–10% (w/w). Two types of whole wheat flours, i.e. spring wheat flour (WFS) and winter wheat flour (WFW), were used. Minimum noise fraction combined with n-Dimensional visualiser tool was applied on light intensity calibrated hyperspectral images for preliminary discrimination. Competitive adaptive reweighted sampling (CARS) was applied for optimal wavelength selection. Partial least squares regression (PLSR) models with standard normal variate followed by Savitzky–Golay first derivatives had the best performance, with coefficients of determination of prediction (R2p) of 0.993 and 0.991, and root mean square error of prediction (RMSEP) of 0.251% and 0.285%, respectively for contaminated WFS and WFW samples. Prediction maps based on PLSR models permitted visualising spatial variations in the concentration of peanut contamination. The results indicated that near-infrared HSI has the potential to detect low-level peanut contamination in whole wheat flour.